GraphQL gives clients exactly the data they need - no more, no less. One endpoint, typed schema, introspection. But the flexibility that makes it powerful also makes it dangerous. Without proper controls, clients can craft queries that bring down your server. This skill covers schema design, resolvers, DataLoader for N+1 prevention, federation for microservices, and client integration with Apollo/urql. Key insight: GraphQL is a contract. The schema is the API documentation. Design it carefully.
GraphQL gives clients exactly the data they need - no more, no less. One
endpoint, typed schema, introspection. But the flexibility that makes it
powerful also makes it dangerous. Without proper controls, clients can
craft queries that bring down your server.
This skill covers schema design, resolvers, DataLoader for N+1 prevention,
federation for microservices, and client integration with Apollo/urql.
Key insight: GraphQL is a contract. The schema is the API documentation.
Design it carefully.
2025 lesson: GraphQL isn't always the answer. For simple CRUD, REST is
simpler. For high-performance public APIs, REST with caching wins. Use
GraphQL when you have complex data relationships and diverse client needs.
Principles
Schema-first design - the schema is the contract
Prevent N+1 queries with DataLoader
Limit query depth and complexity
Use fragments for reusable selections
Mutations should be specific, not generic update operations
Errors are data - use union types for expected failures
Nullability is meaningful - design it intentionally
Capabilities
graphql-schema-design
graphql-resolvers
graphql-federation
graphql-subscriptions
graphql-dataloader
graphql-codegen
apollo-server
apollo-client
urql
Scope
database-queries -> postgres-wizard
authentication -> authentication-oauth
rest-api-design -> backend
websocket-infrastructure -> backend
Tooling
Server
@apollo/server - When: Apollo Server v4 Note: Most popular GraphQL server
graphql-yoga - When: Lightweight alternative Note: Good for serverless
const LOGIN = gql mutation Login($email: String!, $password: String!) { login(email: $email, password: $password) { ... on LoginSuccess { user { id name } token } ... on InvalidCredentials { message } ... on AccountLocked { message unlockAt } } };
// Handle all cases
const result = data.login;
switch (result.__typename) {
case 'LoginSuccess':
setToken(result.token);
redirect('/dashboard');
break;
case 'InvalidCredentials':
setError(result.message);
break;
case 'AccountLocked':
setError(${result.message}. Try again at ${result.unlockAt});
break;
}
Sharp Edges
Each resolver makes separate database queries
Severity: CRITICAL
Situation: You write resolvers that fetch data individually. A query for
10 posts with authors makes 11 database queries. For 100 posts,
that's 101 queries. Response time becomes seconds.
Symptoms:
Slow API responses
Many similar database queries in logs
Performance degrades with list size
Why this breaks:
GraphQL resolvers run independently. Without batching, the author
resolver runs separately for each post. The database gets hammered
with repeated similar queries.
Recommended fix:
USE DATALOADER
import DataLoader from 'dataloader';
// Create loader per request
const userLoader = new DataLoader(async (ids) => {
const users = await db.user.findMany({
where: { id: { in: ids } }
});
// IMPORTANT: Return in same order as input ids
const userMap = new Map(users.map(u => [u.id, u]));
return ids.map(id => userMap.get(id));
});
// Use in resolver
const resolvers = {
Post: {
author: (post, _, { loaders }) =>
loaders.userLoader.load(post.authorId)
}
};
Key points:
1. Create new loaders per request (for caching scope)
2. Return results in same order as input IDs
3. Handle missing items (return null, not skip)
Deeply nested queries can DoS your server
Severity: CRITICAL
Situation: Your schema has circular relationships (user.posts.author.posts...).
A client sends a query 20 levels deep. Your server tries to resolve
it and either times out or crashes.
Symptoms:
Server timeouts on certain queries
Memory exhaustion
Slow response for nested queries
Why this breaks:
GraphQL allows clients to request any valid query shape. Without
limits, a malicious or buggy client can craft queries that require
exponential work. Even legitimate queries can accidentally be too deep.
Recommended fix:
LIMIT QUERY DEPTH AND COMPLEXITY
import depthLimit from 'graphql-depth-limit';
import { createComplexityLimitRule } from 'graphql-validation-complexity';
const server = new ApolloServer({
typeDefs,
resolvers,
validationRules: [
// Limit nesting depth
depthLimit(10),
Introspection enabled in production exposes your schema
Severity: HIGH
Situation: You deploy to production with introspection enabled. Anyone can
query your schema, discover all types, mutations, and field names.
Attackers know exactly what to target.
Symptoms:
Schema visible via introspection query
GraphQL Playground accessible in production
Full type information exposed
Why this breaks:
Introspection is essential for development and tooling, but in
production it's a roadmap for attackers. They can find admin
mutations, internal fields, and deprecated but still working APIs.
const server = new ApolloServer({
typeDefs,
resolvers,
persistedQueries: {
cache: new InMemoryLRUCache()
}
});
Authorization only in schema directives, not resolvers
Severity: HIGH
Situation: You rely entirely on @auth directives for authorization. Someone
finds a way around the directive, or complex business rules don't
fit in a simple directive. Authorization fails.
Symptoms:
Unauthorized access to data
Business rules not enforced
Directive-only security bypassed
Why this breaks:
Directives are good for simple checks but can't handle complex
business logic. "User can edit their own posts, or any post in
groups they moderate" doesn't fit in a directive.
Recommended fix:
AUTHORIZE IN RESOLVERS
// Simple check in resolver
Mutation: {
deletePost: async (_, { id }, { user, db }) => {
if (!user) {
throw new AuthenticationError('Must be logged in');
}
const post = await db.post.findUnique({ where: { id } });
if (!post) {
throw new NotFoundError('Post not found');
}
// Business logic authorization
const canDelete =
post.authorId === user.id ||
user.role === 'ADMIN' ||
await userModeratesGroup(user.id, post.groupId);
if (!canDelete) {
throw new ForbiddenError('Cannot delete this post');
}
return db.post.delete({ where: { id } });
}
}
// Helper for field-level authorization
User: {
email: (user, _, { currentUser }) => {
// Only show email to self or admin
if (currentUser?.id === user.id || currentUser?.role === 'ADMIN') {
return user.email;
}
return null;
}
}
Authorization on queries but not on fields
Severity: HIGH
Situation: You check if a user can access a resource, but not individual
fields. User A can see User B's public profile, and accidentally
also sees their private email and phone number.
Symptoms:
Sensitive data exposed
Privacy violations
Field data visible to wrong users
Why this breaks:
Field resolvers run after the parent is returned. If the parent
query returns a user, all fields are resolved - including sensitive
ones. Each sensitive field needs its own auth check.
// Private fields - check access
email: (user, _, { currentUser }) => {
if (!currentUser) return null;
if (currentUser.id === user.id) return user.email;
if (currentUser.role === 'ADMIN') return user.email;
return null;
},
phoneNumber: (user, _, { currentUser }) => {
if (currentUser?.id !== user.id) return null;
return user.phoneNumber;
},
// Or throw instead of returning null
privateData: (user, _, { currentUser }) => {
if (currentUser?.id !== user.id) {
throw new ForbiddenError('Not authorized');
}
return user.privateData;
}
}
};
Non-null field failure nullifies entire parent
Severity: MEDIUM
Situation: You make fields non-null for convenience. A resolver throws or
returns null. The error propagates up, nullifying parent objects,
until the whole query response is null or errors out.
Symptoms:
Queries return null unexpectedly
One error affects unrelated fields
Partial data can't be returned
Why this breaks:
GraphQL's null propagation means if a non-null field can't resolve,
its parent becomes null. If that parent is also non-null, it
propagates further. One failing field can break an entire response.
Recommended fix:
DESIGN NULLABILITY INTENTIONALLY
WRONG: Everything non-null
type User {
id: ID!
name: String!
email: String!
avatar: String! # What if no avatar?
lastLogin: DateTime! # What if never logged in?
}
RIGHT: Nullable where appropriate
type User {
id: ID! # Always exists
name: String! # Required field
email: String! # Required field
avatar: String # Optional - may not exist
lastLogin: DateTime # Nullable - may be null
}
For lists:
[User!]! - Non-null list of non-null users (recommended)
[User!] - Nullable list of non-null users
[User]! - Non-null list of nullable users (rarely useful)
[User] - Nullable list of nullable users (avoid)
Rule of thumb:
- Non-null if always present and failure should fail query
- Nullable if optional or failure shouldn't break response
Expensive queries treated same as cheap ones
Severity: MEDIUM
Situation: Every query is processed the same. A simple user(id) query uses
the same resources as users(first: 1000) { posts { comments } }.
Expensive queries starve out cheap ones.
Symptoms:
Expensive queries slow everything
No way to prioritize queries
Rate limiting is ineffective
Why this breaks:
Not all GraphQL operations are equal. Fetching 1000 users with
nested data is orders of magnitude more expensive than fetching
one user. Without cost analysis, you can't rate limit properly.
Recommended fix:
QUERY COST ANALYSIS
import { createComplexityLimitRule } from 'graphql-validation-complexity';
// For rate limiting by cost
const costPlugin = {
requestDidStart() {
return {
didResolveOperation({ request, document }) {
const cost = calculateQueryCost(document);
if (cost > 1000) {
throw new Error(Query too expensive: ${cost});
}
// Track cost for rate limiting
rateLimiter.consume(request.userId, cost);
}
};
}
};
Subscriptions not properly cleaned up
Severity: MEDIUM
Situation: Clients subscribe but don't unsubscribe cleanly. Network issues
leave orphaned subscriptions. Server memory grows as dead
subscriptions accumulate.
Symptoms:
Memory usage grows over time
Dead connections accumulate
Server slows down
Why this breaks:
Each subscription holds server resources. Without proper cleanup
on disconnect, resources accumulate. Long-running servers
eventually run out of memory.
Recommended fix:
PROPER SUBSCRIPTION CLEANUP
import { PubSub, withFilter } from 'graphql-subscriptions';
import { WebSocketServer } from 'ws';
import { useServer } from 'graphql-ws/lib/use/ws';
const pubsub = new PubSub();
// Track active subscriptions
const activeSubscriptions = new Map();
const wsServer = new WebSocketServer({
server: httpServer,
path: '/graphql'
});
